1. Wilma Bainbridge Tencia Lee Kendra Leigh
Machine Learning
Wilma Bainbridge
Tencia Lee
Kendra Leigh

2. What is Machine Learning?
Machine learning is the process in which a machine changes its structure, program, or data in response to external information in such a way that its expected future performance improves.
Learning by machines can overlap with simpler processes, such as the addition of records to a database, but other cases are clear examples of what is called “learning,” such as a speech recognition program improving after hearing samples of a person’s speech.

3. Components of a Learning Agent
Curiosity Element – problem generator; knows what the agent wants to achieve, takes risks (makes problems) to learn from
Learning Element – changes the future actions (the performance element) in accordance with the results from the performance analyzer
Performance Element – choosing actions based on percepts
Performance Analyzer – judges the effectiveness of the action, passes info to the learning element

4. Why is machine learning important?
Or, why not just program a computer to know everything it needs to know already?
Many programs or computer-controlled robots must be prepared to deal with things that the creator would not know about, such as game-playing programs, speech programs, electronic “learning” pets, and robotic explorers. Here, they would have access to a range of unpredictable knowledge and thus would benefit from being able to draw conclusions independently.

5. Relevance to AI
Helps programs handle new situations based on the input and output from old ones
Programs designed to adapt to humans will learn how to better interact
Could potentially save bulky programming and attempts to make a program “foolproof”
Makes nearly all programs more dynamic and more powerful while improving the efficiency of programming.

6. Approaches to Machine Learning
Boolean logic and resolution
Evolutionary machine learning – many algorithms / neural networks are generated to solve a problem, the best ones survive
Statistical learning
Unsupervised learning – algorithm that models outputs from the input, knows nothing about the expected results
Supervised learning – algorithm that models outputs from the input and expected output
Reinforcement learning – algorithm that models outputs from observations

7. Current Machine Learning Research
Almost all types of AI are developing machine learning, since it makes programs dynamic.
Examples:
Facial recognition – machines learn through many trials what objects are and aren’t faces
Language processing – machines learn the rules of English through example; some AI chatterbots start with little linguistic knowledge but can be taught almost any language through extensive conversation with humans

8. Future of Machine Learning
Gaming – opponents will be able to learn from the player’s strategies and adapt to combat them
Personalized gadgets – devices that adapt to their owner as he changes (gets older, gets different tastes, changes his modes)
Exploration – machines will be able to explore environments unsuitable for humans and quickly adapt to strange properties

9. Problems in Machine Learning
Learning by Example:
Noise in example classification
Correct knowledge representation
Heuristic Learning
Incomplete knowledge base
Continuous situations in which there is no absolute answer
Case-based Reasoning
Human knowledge to computer representation

10. Problems in Machine Learning
Grammar – meaning pairs
new rules must be relearned a number of times to gain “strength”
Conceptual Clustering
Definitions can be very complicated
Not much predictive power

11. Successes in Research
ARCH by P.H. Winston in which positive and negative examples are used to explain the concept
D. B. Lenat’s pioneering work in heuristics with incomplete knowledge base: RLL language and EURISKO system
LAS by Anderson (1977) & AMBER by Langley (1982) simulate aspects of grammar learning

12. Successes continued… Aspects of daily life using machine learning
Optical character recognition
Handwriting recognition
Speech recognition
Automated steering
Assess credit card risk
Filter news articles
Refine information retrieval
Data mining

13. Bibliography http://robotics.stanford.edu/people/nilsson/mlbook.html
Shapiro, Stuart C. and David Eckroth (ed.) “Machine Learning” Encyclopedia of Artificial
Intelligence. New York: John Wiley & Sons. © 1987.

14. Any Questions?